Soft Computing Approaches to Stock Forecasting: A Survey

Soft computing techniques has been effectively applied in business, engineering, medical domain to solve problems in the past decade. However, this paper focuses on censoring the application of soft computing techniques for stock market prediction in the last decade (2010 - todate). Over a hundred published articles on stock price prediction were reviewed. The survey is done by grouping these published articles into: the stock market surveyed, input variable choices, summary of modelling technique applied, comparative studies, and summary of performance measures. This survey aptly shows that soft computing techniques are widely used and it has demonstrated widely acceptability to accurately use for predicting stock price and stock index behavior worldwide

Forecasting CPI in Sweden

This research uses annual time series data on CPI in Sweden from 1960 to 2017, to model and forecast CPI using the Box – Jenkins ARIMA technique. Diagnostic tests indicate that the W series is I (1). The study presents the ARIMA (1, 1, 0) model for predicting CPI in Sweden. The diagnostic tests further imply that the presented optimal model is stable as expected. The results of the study apparently show that CPI in Sweden is likely to continue on an upwards trajectory in the next ten years. The study encourages policy makers to make use of tight monetary and fiscal policy measures in order to control inflation in Sweden

Predicting CPI in Panama

This study uses annual time series data on CPI in Panama from 1960 to 2017, to model and forecast CPI using the Box – Jenkins ARIMA technique. Diagnostic tests indicate that the P series is I (1). The study presents the ARIMA (1, 1, 0) model for predicting CPI in Panama. The diagnostic tests further imply that the presented optimal model is actually stable and acceptable for forecasting CPI in Panama. The results of the study apparently show that CPI in Panama is likely to continue on an upwards trajectory in the next 10 years. The study encourages policy makers to make use of tight monetary and fiscal policy measures in order to deal with inflation in Panama

Forecasting CPI in Sweden

This research uses annual time series data on CPI in Sweden from 1960 to 2017, to model and forecast CPI using the Box – Jenkins ARIMA technique. Diagnostic tests indicate that the W series is I (1). The study presents the ARIMA (1, 1, 0) model for predicting CPI in Sweden. The diagnostic tests further imply that the presented optimal model is stable as expected. The results of the study apparently show that CPI in Sweden is likely to continue on an upwards trajectory in the next ten years. The study encourages policy makers to make use of tight monetary and fiscal policy measures in order to control inflation in Sweden

Forecasting Australian CPI using ARIMA models

This research uses annual time series data on CPI in Australia from 1960 to 2017, to model and forecast CPI using the Box – Jenkins ARIMA technique. Diagnostic tests indicate that the A series is I (1). The study presents the ARIMA (1, 1, 0) model for predicting CPI in Australia. The diagnostic tests further imply that the presented optimal model is stable and acceptable. The results of the study apparently show that CPI in Australia is likely to continue on an upwards trend in the next decade. The study basically encourages policy makers to make use of tight monetary and fiscal policy measures in order to control inflation in Australia

Predicting CPI in Panama

This study uses annual time series data on CPI in Panama from 1960 to 2017, to model and forecast CPI using the Box – Jenkins ARIMA technique. Diagnostic tests indicate that the P series is I (1). The study presents the ARIMA (1, 1, 0) model for predicting CPI in Panama. The diagnostic tests further imply that the presented optimal model is actually stable and acceptable for forecasting CPI in Panama. The results of the study apparently show that CPI in Panama is likely to continue on an upwards trajectory in the next 10 years. The study encourages policy makers to make use of tight monetary and fiscal policy measures in order to deal with inflation in Panama

Modeling and forecasting CPI in Mauritius

This paper uses annual time series data on CPI in Mauritius from 1963 to 2017, to model and forecast CPI using the Box – Jenkins ARIMA technique. Diagnostic tests indicate that the Z series is I (2). The study presents the ARIMA (0, 2, 3) model for predicting CPI in Mauritius. The diagnostic tests further imply that the presented optimal model is actually stable and acceptable for predicting CPI in Mauritius. The results of the study apparently show that CPI in Mauritius is likely to continue on a very sharp upwards trajectory in the next decade. The study basically encourages policy makers to make use of tight monetary and fiscal policy measures in order to control inflation in Mauritius

Predicting consumer price index in Saudi Arabia

This paper uses annual time series data on CPI in Japan from 1963 to 2017, to model and forecast CPI using the Box – Jenkins ARIMA technique. Diagnostic tests indicate that the Y series is I (2). The study presents the ARIMA (0, 2, 1) model for predicting CPI in Saudi Arabia. The diagnostic tests further imply that the presented optimal model is actually stable and acceptable for predicting CPI in Saudi Arabia. The results of the study apparently show that CPI in Saudi Arabia is likely to be relatively high in the next decade. The study encourages policy makers to make use of tight monetary and fiscal policy measures in order to deal with inflation in Saudi Arabia

Analyzing CPI dynamics in Italy

This research uses annual time series data on CPI in Italy from 1960 to 2017, to model and forecast CPI using the Box – Jenkins ARIMA technique. Diagnostic tests indicate that the T series is I (2). The study presents the ARIMA (0, 2, 1) model for predicting CPI in Italy. The diagnostic tests further imply that the presented optimal model is actually stable and acceptable for predicting CPI in Italy over the period under study. The results of the study apparently show that CPI in Italy is likely to continue on an upwards trajectory in the next decade. The study basically encourages policy makers to make use of tight monetary and fiscal policy measures in order to control inflation in Italy

Modeling and forecasting CPI in Myanmar: An application of ARIMA models

This research uses annual time series data on CPI in Myanmar from 1960 to 2017, to model and forecast CPI using the Box – Jenkins ARIMA technique. Diagnostic tests indicate that the M series is I (2). The study presents the ARIMA (2, 2, 1) model for predicting CPI in Myanmar. The diagnostic tests further imply that the presented optimal model is stable and acceptable in modeling CPI in Myanmar. The results of the study apparently show that CPI in Myanmar is likely to continue on an upwards trajectory in the next decade. The study encourages policy makers to make use of tight monetary and fiscal policy measures in order to deal with inflation in Myanmar